Year: 2019
Type: conference-abstract
Abstract: The ultrasonography of fetal heart is not easy because of fast heart beats and small organ. Some views were recommended to pick up congenital heart disease (CHD). But, it is difficult to take the correct image of views. Thus the rate of antenatal detection for CHD likely depends on skills of each sonographer. The aim of this study is to ascertain whether a novel artificial Intelligence (AI) technology systemwhich we have developed is helpful for segmental diagnosis of normal fetal heat. 2378 frames from 51 normal fetal cardiac screening movies at 18-20 weeks, were used as a training dataset for machine learning. 701 frames from 28 normal fetal cardiac screening movie used as test data. The movie included the hole images of fetal heart from stomach to base of heart. Correct positions of the heart and peripheral organs were annotated. The positions were following crux, ventricular septum, right atrium, tricuspid valve, right ventricle, left atrium, mitral valve, left ventricle, pulmonary artery, ascending aorta, superior vena cava, descending aorta, stomach spine, umbilical vein, inferior vena cava, pulmonary vein, ductus arteriosus. We calculated the accuracy of each parts of normal fetal heart ultrasound screening movie. The recall ratio of crux, ventricular septum, left ventricle and atrium, right ventricle and atrium, ascending aorta, pulmonary artery, stmach and spine were 97.1%, 69.3%, 96.6% and 90.6%, 84.8% and 96.9%, 61.9%, 100%, 100% and 100%, respectively. Supporting information can be found in the online version of this abstract Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
DOI: 10.1002/uog.20945
Authors: R. Matsuoka, M. Komatsu, A. Sakai, S. Yasutomi, T. Arakaki, M. Tokunaka, R. Komatsu, R. Hamamoto, A. Sekizawa
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W2977948637